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Scientific Research


3D visual recognition sorting

Through the integration of the Universal Robots UR5 robot arm integrated Pickit series camera, a more intelligent and versatile robot vision task solution is realized, such as: easy and automated execution of pick and place tasks, material handling/packag

Project course

Simple and easy-to-use robot 3D vision solution. We provide standard solutions, which do not require additional expensive visual technical support. Users do not need CAD templates to define new workpieces and build their own systems. We provide three products to help you achieve automated palletizing and sorting tasks. All products include a complete solution: 3D camera, processing software, industrial computer, calibration tools and necessary cables.

【Experimental teaching goal 】



【Main instruments and equipment 】

The main hardware of the program includes: 1 UR5 robotic arm (can be other UR models), PICK-IT M-HD 3D vision system, and a 2-finger or 3-finger gripper. The UR+ plug-in allows the two to be perfectly combined to achieve various 3D visual sorting tasks. 

【Experimental project setup 】



1)Design of Motion Controller of Mobile Robot

Röhrig C, Heß D, Künemund F. Motion controller design for a mecanum wheeled mobile manipulator[C]//2017 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2017: 444-449.

2)Real-time motion control for upper limb tracking

Omarali B, Taunyazov T, Bukeyev A, et al. Real-Time Predictive Control of an UR5 Robotic Arm Through Human Upper Limb Motion Tracking[C]//Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. 2017: 237-238.

3Use UR to imitate human writing

Miatliuk K, Wolniakowski A, Diaz M, et al. Universal robot employment to mimic human writing[C]//2019 20th International Carpathian Control Conference (ICCC). IEEE, 2019: 1-5.

Kinematics and dynamics

1)Calibration of UR5 manipulator based on kinematics model

Liang B, Cheng Y, Zhu X, et al. Calibration of UR5 manipulator based on kinematic models[C]//2018 chinese control and decision conference (CCDC). IEEE, 2018: 3552-3557.

2UR5 dynamic modeling

Kebria P M, Al-Wais S, Abdi H, et al. Kinematic and dynamic modelling of UR5 manipulator[C]//2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, 2016: 004229-004234.

3Based on singularity-robust multi-task priority inverse kinematics framework

Moe S, Antonelli G, Pettersen K Y, et al. Experimental results for set-based control within the singularity-robust multiple task-priority inverse kinematics framework[C]//2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2015: 1233-1239.

4)A new method for inverse kinematics

Moe S, Antonelli G, Teel A R, et al. Set-based tasks within the singularity-robust multiple task-priority inverse kinematics framework: General formulation, stability analysis, and experimental results[J]. Frontiers in Robotics and AI, 2016, 3: 16.

Vision, calibration, perception

1)Image vision automatic calibration system based on UR

Jian B L, Tsai C S, Kuo Y C, et al. An image vision and automatic calibration system for universal robots[J]. Journal of Low Frequency Noise, Vibration and Active Control, 2019: 1461348419874925.

2New planning method based on UR

Tatsubori M, Munawar A, Moriyama T. Design and Implementation of Linked Planning Domain Definition Language[J]. arXiv preprint arXiv:1912.07834, 2019.

3)Hand-eye calibration

Li J, Li X, Dun A, et al. Hand-eye calibration for flexible manipulator[C]//Journal of Physics: Conference Series. IOP Publishing, 2019, 1187(3): 032097.

4Adaptive learning based on UR

Inoue T, Chaudhury S, De Magistris G, et al. Transfer learning from synthetic to real images using variational autoencoders for robotic applications[J]. arXiv preprint arXiv:1709.06762, 2017.

5UR-based visual tracking

Ramachandruni K, Jaiswal S, Shah S V. Vision-based control of UR5 robot to track a moving object under occlusion using Adaptive Kalman Filter[M]//Proceedings of the Advances in Robotics 2019. 2019: 1-6.


1)Automatic adjustment and configuration of path planning algorithms

Burger R, Bharatheesha M, van Eert M, et al. Automated tuning and configuration of path planning algorithms[C]//2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 4371-4376.

2)Robot task planning and evaluation method

Paxton C, Jonathan F, Hundt A, et al. Evaluating methods for end-user creation of robot task plans[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018: 6086-6092.

3)Predictive control of deep learning for human motion tracking

Agravante D J, De Magistris G, Munawar A, et al. Deep learning with predictive control for human motion tracking[J]. arXiv preprint arXiv:1808.02200, 2018.

4Time optimal planning based on UR collaborative robot

Zhenyi C. Joint Trajectory Time Optimization of Cobot Based on Particle Swarm Optimization[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2019, 616(1): 012015.

5)Multi-manipulator trajectory planning algorithm

Tavares P, Lima J, Costa P, et al. Multiple manipulators path planning using double A[J]. Industrial Robot: An International Journal, 2016.

Medical treatment

1)UR-based rehabilitation robot

Kyrkjebø E, Laastad M J, Stavdahl Ø. Feasibility of the UR5 Industrial Robot for Robotic Rehabilitation of the Upper Limbs After Stroke[C]//2018 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2018: 1-6.

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Donghu Robot Laboratory, 2nd Floor, Baogu Innovation and Entrepreneurship Center,Wuhan City,Hubei Province,China

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